Abstract
This chapter describes a modeling framework for using isoscapes to describe probable geographic origins of sampled material. The approach first inverts an isoscape model and then adds known or estimated components of variance to create a single continuous posterior probability density that describes the probability of any given location as the origin. The posterior density is defined analytically or by Monte Carlo methods, depending on the complexity of the variance structure. The model must be trained with material of known origin and can be used to determine spatially-explicit probability densities for geographic origin of individual samples. Individual densities can be used in aggregate to find spatial structure in the geographic origins for populations of samples. To illustrate the approach, I explore simulated data for a hypothetical example from wildlife forensics involving the determination of cross-seasonal connectivity in a migratory bird species.
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Acknowledgements
I thank the editors for the opportunity to write this chapter. Comments from Gabe Bowen, Jason West, and two anonymous reviewers substantially improved earlier versions of this manuscript. I thank Mirgate and Basin for funding my travel to the Isoscapes meeting in Santa Barbara, CA where I presented the methods described here. In addition, I am grateful to Fritz Knopf, Colleen Webb, Cyndi Kester, Craig Stricker, Craig Johnson, Len Wassenaar, Keith Hobson, Ryan Norris, Pete Marra, Jeff Kelly, and Carlos Martinez del Rio for discussions that shaped both the direction and presentation of this work.
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Wunder, M.B. (2010). Using Isoscapes to Model Probability Surfaces for Determining Geographic Origins. In: West, J., Bowen, G., Dawson, T., Tu, K. (eds) Isoscapes. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-3354-3_12
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DOI: https://doi.org/10.1007/978-90-481-3354-3_12
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